Big Data Engineer

Data Pipeline Development & Operations: Design, build, handle, and monitor Spark data pipelines for large-volume data lake ingestion (handling hundreds of TBs).

Platform & Cost Optimization: Continuously optimize the performance, reliability, and costs of big data pipelines and infrastructure.

Databricks, Snowflake & Cloud Engineering: Deeply leverage Databricks, Snowflake, or AWS Redshift, alongside cloud provider solutions (AWS S3, Glue/Athena, Lambda) for scalable data engineering.

Lakehouse Architecture: Demonstrate strong expertise in managing, creating, and optimizing Delta Lake or Apache Iceberg tables.

Secure Data Management: Apply security-by-design, data governance, and compliance best practices across storage, compute, and sharing layers.

7+ years of experience in data engineering or backend data platforms, with at least 3 years of hands-on experience building and operating big data pipelines.

Hands-on engineer background with proven track record handling large volumes of data (at least hundreds of TB).

Deep knowledge of at least one data lake platform among Databricks, Snowflake, or AWS Redshift.

Strong expertise in Apache Spark (creation, handling, and monitoring of pipelines) and lakehouse table formats (Delta Lake or Apache Iceberg).

Strong Python or Java and SQL skills, cloud infrastructure (AWS S3, Athena, Glue), and IaC/observability practices.

Solid understanding of cost management principles, data security, governance, and data modeling.

Equivalent engineering school degree or Master’s degree in Computer Science, Data Science, or Applied Mathematics.

Fluent in English and French, with clear communication, ownership mindset, and strong collaborative skills.